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Learning Types

32,670 papers (1 classified directly here)

Representation Learning (5,307) Multimodal Learning (3,969) Self-Supervised Learning (3,492) Transfer Learning (2,890) Adversarial Learning (2,199) Deep Learning (1,883) Contrastive Learning (1,730) Reinforcement Learning (1,500) Knowledge Distillation (1,494) Fine-Tuning (1,117) Multi-Task Learning (1,069) Domain Adaptation (977) Zero-Shot Learning (758) Unsupervised Learning (734) Few-Shot Learning (694) Classification (621) Retrieval-Augmented Generation (606) Data Augmentation (583) Semi-Supervised Learning (450) Weakly Supervised Learning (429) Continual Learning (408) In-Context Learning (387) Sequence Modeling (375) Meta-Learning (252) Metric Learning (232) Ensemble Learning (223) Federated Learning (215) Multi-Lingual Learning (199) Robustness (197) Parameter-Efficient Fine-Tuning (190) Imitation Learning (180) Prompt Engineering (170) Chain-of-Thought Reasoning (153) Curriculum Learning (146) Synthetic Data Generation (143) Reinforcement Learning from Human Feedback (141) Multi-Label Classification (123) Regression (117) Generative Model (109) Semantic Segmentation (106) Domain Generalization (105) Uncertainty Quantification (98) Controllable Generation (93) Text Generation (86) Neural Architecture Search (80) Object Detection (80) Image Generation (79) Multi-Agent Systems (76) Feature Learning (74) Preference Learning (73) Machine Translation (71) Out-of-Distribution Detection (68) Machine Unlearning (63) Generation (61) Reasoning (54) Multi-View Learning (53) Time Series (52) Instruction Tuning (50) Distributed Learning (50) Image Restoration (49) Chain-of-Thought (48) Active Learning (47) Text Classification (43) Distribution Shift (43) Structured Prediction (42) Clustering (41) Image Classification (40) Model Merging (37) Sparse Learning (36) Sequence Labeling (36) Multi-Instance Learning (35) Anomaly Detection (33) Image Processing (33) Code Generation (31) Fairness (30) Robust Learning (28) Multi-Domain Learning (27) Supervised Learning (26) Model-Based RL (25) Natural Language Generation (24) Privacy (21) Multi-Objective Optimization (19) Multi-Scale Learning (19) Neural Machine Translation (18) Causal Inference (14) Instance Segmentation (9) Interactive Learning (9) Multi-Label Learning (9) Multi-Source Learning (9) Multi-Class Classification (8)
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Papers (including subtopics)

PP-Brep: Few-Shot B-rep Classification with Hybrid Graph Representation CVPR 2026 PPM-CLIP: Probabilistic Prompt Modeling for Generalizable AI-Generated Image Detection CVPR 2026 Practical Guidelines for Model Merging in LLMs Pre-Training ACL 2026 Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation ACL 2026 Pre-Deployment Advertisement Ranking under Data Scarcity via Context-Aware Criteria Generation with VLMs ACL 2026 Pref-CTRL: Preference Driven LLM Alignment using Representation Editing ACL 2026 Preference-Aligned LoRA Merging: Preserving Subspace Coverage and Addressing Directional Anisotropy CVPR 2026 Premier: Personalized Preference Modulation with Learnable User Embedding in Text-to-Image Generation CVPR 2026 Preserving Compositionality for Robust Multi-Subject Personalization in Text-to-Image Generation UAI 2026 PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues ACL 2026 PRISM: Learning a Shared Primitive Space for Transferable Skeleton Action Representation CVPR 2026 PRISM: Probabilistic Reward Model with Inherent Structural Modeling ACL 2026 PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation ACL 2026 Privacy Collapse: Benign Fine-Tuning Can Break Contextual Privacy in Language Models ACL 2026 Privacy-preserving Prosody Representation Learning ACL 2026 PriVi: Towards a General-Purpose Video Model for Primate Behavior in the Wild CVPR 2026 PrivSynth: Alternating and Control-Based Optimization for Privacy and Utility in Synthetic Data CVPR 2026 PR-MaGIC: Prompt Refinement Via Mask Decoder Gradient Flow For In-Context Segmentation CVPR 2026 Probabilistic Feature Imputation and Uncertainty-Aware Multimodal Federated Aggregation MIDL 2026 Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula JMLR 2026 Procedure Learning via Regularized Gromov-Wasserstein Optimal Transport WACV 2026 Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards ACL 2026 ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation ACL 2026 Progress by Pieces: Test-Time Scaling for Autoregressive Image Generation CVPR 2026 Progressive Cross-Modal Causal Intervention for Long-Term Action Recognition CVPR 2026